Model Hyper Parameters
Training dates 2016-09-01 to 2019-12-30
Lift Analysis
Lift is the expected contribution of the channel to the overall prediction.
“If we removed the channel, what percent of total Visits would we lose?”
|
|
2017
|
2018
|
2019
|
|
(Intercept)
|
-44%
|
-37.9%
|
-32.7%
|
|
tv
|
4%
|
3.1%
|
2.8%
|
|
semBR
|
2%
|
1.4%
|
1.2%
|
|
semNB
|
2%
|
2.3%
|
1.9%
|
|
app
|
4%
|
4.7%
|
6.3%
|
|
social
|
6%
|
6.2%
|
6.9%
|
|
digital
|
1%
|
1.4%
|
1.6%
|
|
email_marketing
|
0%
|
-0.1%
|
-0.1%
|
|
email_operational
|
5%
|
2.6%
|
3.3%
|
|
email_savedsearch
|
4%
|
5.6%
|
4.9%
|
|
SEO_bigcity
|
61%
|
52.6%
|
45.1%
|
|
SEO_hdp
|
-1%
|
-0.5%
|
-0.4%
|
|
push_android
|
-1%
|
-0.8%
|
-1.0%
|
|
push_ios
|
2%
|
2.9%
|
4.7%
|
|
nonbrand_queries
|
42%
|
35.3%
|
32.1%
|

CPA Analysis
|
date
|
tv
|
semBR
|
semNB
|
app
|
social
|
digital
|
|
2016-09-01
|
$0.16
|
$0.04
|
$0.13
|
$0.02
|
$0.04
|
$0.11
|
|
2017-01-01
|
$0.23
|
$0.04
|
$0.13
|
$0.02
|
$0.04
|
$0.12
|
|
2018-01-01
|
$0.26
|
$0.04
|
$0.13
|
$0.03
|
$0.04
|
$0.12
|
|
2019-01-01
|
$0.21
|
$0.05
|
$0.13
|
$0.03
|
$0.04
|
$0.13
|



Error Plot

Variable Analysis
Variable Correlation

Variable plots
Owned
## [1] "email_marketing" "email_operational" "email_savedsearch"
## [4] "push_android" "push_ios"

Search
## [1] "SEO_bigcity" "SEO_hdp" "nonbrand_queries"

Apendix
Seasonal Variable
|
|
Estimate
|
t value
|
Pr(>|t|)
|
|
christmas
|
-2,222,067
|
-11.797
|
0.000
|
|
easter
|
-1,316,868
|
-6.524
|
0.000
|
|
fathers
|
-851,984
|
-4.203
|
0.000
|
|
halloween
|
-924,387
|
-3.747
|
0.000
|
|
independence
|
-2,235,368
|
-7.768
|
0.000
|
|
labor
|
-521,534
|
-3.573
|
0.000
|
|
martin
|
314,085 *
|
1.121
|
0.262
|
|
memorial
|
-1,472,329
|
-8.555
|
0.000
|
|
mothers
|
-958,076
|
-4.697
|
0.000
|
|
new
|
-1,271,231
|
-6.297
|
0.000
|
|
presidents
|
237,153 *
|
0.844
|
0.399
|
|
super
|
-829,013
|
-2.938
|
0.003
|
|
thanksgiving
|
-1,599,657
|
-11.482
|
0.000
|
|
valentines
|
-1,380,763
|
-4.951
|
0.000
|
|
year2017
|
884,350
|
10.702
|
0.000
|
|
year2018
|
2,405,974
|
20.291
|
0.000
|
|
year2019
|
3,353,969
|
19.862
|
0.000
|
|
month2
|
1,090,472
|
13.320
|
0.000
|
|
month3
|
1,500,339
|
18.681
|
0.000
|
|
month4
|
1,956,723
|
21.389
|
0.000
|
|
month5
|
1,536,695
|
16.354
|
0.000
|
|
month6
|
1,152,536
|
11.416
|
0.000
|
|
month7
|
1,681,297
|
16.724
|
0.000
|
|
month8
|
1,598,737
|
17.838
|
0.000
|
|
month9
|
917,856
|
11.517
|
0.000
|
|
month10
|
788,983
|
9.028
|
0.000
|
|
month11
|
212,252
|
2.085
|
0.037
|
|
month12
|
-802,582
|
-8.478
|
0.000
|
|
wdayMonday
|
4,624 *
|
0.058
|
0.953
|
|
wdaySaturday
|
-216,518
|
-3.090
|
0.002
|
|
wdaySunday
|
-1,052 *
|
-0.012
|
0.991
|
|
wdayThursday
|
44,640 *
|
0.654
|
0.513
|
|
wdayTuesday
|
44,313 *
|
0.598
|
0.550
|
|
wdayWednesday
|
113,255 *
|
1.604
|
0.109
|